Abstract. The control system of the permanent magnet synchronous motor (PMSM) has the characteristics of nonlinear and strong coupling. Therefore, In order to improve the control precision, the paper presents a novel approach of speed control for PMSM using adaptive BP (back-propagations)-PID neural network. The approach consists of two parts: on-line identification based on BP neural network and the adaptive PID controller. Lyapunov theory is used to prove the stability of the control scheme. Simulation results show that this control method can improve the dynamical performance and enhance the static precision of the speed system．

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This paper is concerned with the adaptive fault tolerant synchronization problem for a class of complex interconnected neural networks against sensor failure and coupling failure. As sensor and coupling failure may lead to performance degradation or even instability of the whole network, adaptive approach is proposed to adjust unknown coupling factors for the deteriorated network compensations, as well as to estimate controller parameters to compensate the effects of failed coupling. Through Lyapunov functions and adaptive schemes, three kind of fault tolerant controllers are constructed to ensure the synchronization of the networks in the presence of the network deterioration. Simulation results are given to verify the effectiveness of the proposed method.